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Research2026-06-14 · source-backed
UOJ-Bench uses real competitive-programming submissions to test generation, error-finding, and repair. In single-attempt evaluation, top models fail to identify errors in over 50% of incorrect submissions (arXiv). Test-time scaling pushes success above 90%, but models also flag problems in more than 5% of perfect submissions. So your code-judging agent both misses real bugs and invents fake ones, and only multi-attempt scaling rescues it. Single-pass agent code review is unreliable by the numbers.
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Skill files work because they're specific. They name the exact script, the exact API call, the exact flag your repo needs. That specificity is the whole value, and it's also the thing that quietly stops being true the moment the repo tags a new version. Repo2Skill-Evo measured...
Writer launched Palmyra X6 on August 13 with a number that should reset how you think about agent COGS: 52% lower average cost, 48% better speed, 10% better quality. The model is a post-training variation of Z.ai's open-source GLM-5.2. A US enterprise SaaS vendor built its fla...
SpecFirst splits the loop in two: a spec agent probes the binary and fuses observations with documentation into a structured specification, then a separate synthesis agent codes against that fixed reference. Test pass rates rose 6.9-21.3% and binary exploration coverage 9.4-18...
DSEffi-Bench covers 1,000 instances across 10+ libraries with stress-testing harnesses and human-validated references, evaluated on 16 models (arXiv 2608.30248). GPT-5.4 leads correctness at 66.9% Pass but its 71.7% efficiency score barely beats GPT-5.4-mini's 71.6% despite so...
Skill-α (arXiv 2608.01678) reframes skill generation as RL over sequential edits, decomposing skill construction into individually evaluable changes. The novel signal is a rollback reward that scores each modification by comparing downstream task execution using the original s...
Here's the number that should sit in every "agents will replace engineers" thread: 15.2%. That's the best model. The mean across 15 frontier models is 4.3%. Tencent Hunyuan's Long-Horizon-Terminal-Bench put 15 frontier models against 46 long-horizon terminal tasks across nine...
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